kynnemall / guitar-price-prediction Goto Github PK

1. Installation
2. Project Motivation
3. File Descriptions
4. Results

There should be no necessary libraries to run the code here beyond the Anaconda distribution of Python. The code should run with no issues using Python versions 3.*.

For this project, I was interested in finding out how different features of Les Paul electric guitars affect their price and sales rank on the Thomann website. Thomann stocks a wide range of musical equipment and was the perfect source to get structured information on Les Paul guitars. I used Scrapy, a Python web-scraping framework, to get the product info and create the dataset for this project. I was interestested in using these data to better understand:

1. How does guitar price affect the sales rank?
2. How does the choice of wood for different parts affect the sales rank?
3. Which features of a Les Paul guitar are most important in determining the price?

There are 3 notebooks available here to showcase work related to the above questions. Each notebook explores the dataset to answer the questions outlined above. Markdown cells were used to assist in walking through the thought process for individual steps.

The findings from the first part of this analysis can be found here. Part two will begin soon and should be online in a few weeks!